In collaboration with Jake Schonberger and Stephen Sun

Master in Design Engineering Collaborative Design Engineering Studio II

Harvard University

Feb 2019

improving organ transplant

20 people die each day waiting for an organ transplant, about a third of those people are waiting for a liver transplant. Odds are, their life-saving organ is sitting in an ice box a few states away. The national transplant list in currently (as of 2.14.19) consists of over 114k people, 13.5k of whom are praying for a liver (the second most transplanted organ behind the kidney). With only about 3 in every 1000 recovered organs deemed as ‘transplantable,’ the need for efficient and effective matching and utilization of transplantable organs is imperative to save lives. 

understanding the impact of patient-level organ refusals for DBD livers

HEART

LUNGS

LIVER

PANCREAS

KIDNEY

PROBLEM

Transplant “utilization rate” is the metric used to determined the percentage of available organs that are used in a transplant operations. Historically, the number of people awaiting organs largely exceeds the number of available organs. Currently, roughly 25.7% of organs, and 14.4% of livers approved for donation are discarded due to lack of accessible or unapproved donation recipient.

PROCESS

This study will focus on DBD (Donor by Brain Death) liver transplants in the US, by developing a dataset to understand the volume of incidences where patients are the final refusal factor for a liver transplant. Our focus will be on situations where the liver is provisionally approved and accepted by their surgeon and transplant center.

SOLUTION

Our goal is to a) gather data to determine the volume of incidences where a liver is declined at the patient level, and the associated factors around that decision, b) determine if the volume of the previous data is representative of a significant utilization rate improvement and c) develop & implement methodologies to intervene & help improve the patient level acceptance rate.

THE CURRENT ORGAN DONATION LANDSCAPE

Organ transplantation is a medical procedure surgically transferring a donated organ to the recipient diagnosed with organ failure.

95K
13K
3.8K
1.4K
0.8K
114 K

KIDNEY

PANCREAS

LIVER

LUNGS

HEART

TOTAL PATIENTS ON THE WAITLIST

GENERAL CAUSES FOR A LIVER TRANSPLANT

CAUSES FOR LIVER TRANS.png

FOCUS AREA

FOCUS AREA.png

THE LIVER

We decided to study the transplant process of the liver because it is the most donated and requested organ after the kidney. Around 85% of transplants in the United States occur after a brain deceased donor offers his organ. It encompasses the biggest chunk of liver transplants and it is where we could have the biggest impact. Through our research, we found there was a 14.4% discard rate in available and transplant-approved livers.

REASONS FOR INNEFICIENCIES

Matching is hard

Surgery is difficult

People don't donate enough

Organs have short shelf lives

The process is incredibly complex

LIVER TRANSPLANT PROCEDURAL MAP

Understanding the impact of patient-level refusals in DBD liver transplant on organ utilization rates

RESEARCH GOALS FOR PROPOSAL

>  Quantify the amount of times an organ is declined due to patient-level refusals 

>  Understand how (if at all) this volume impacts the liver utilization rate

>  Map a geographic understanding of region-by-region patient refusals

>  Develop, test & implement new methods to help patients approve livers at higher rates 

GEOGRAPHICAL MAPPING OF 11 REGIONS DETERMINED BY UNOS

GEOGRAPHICAL MAPPING OF LIVER DATA FOR REGIONS 1, 2 AND 9

FOCUS AREA OF SPECIALISTS AND FIELDS

TEAM OF SPECIALISTS EVALUATING PATIENTS

AREAOFFOCUS.png

FIELDS OF EVALUATION FOR LIVER WAITLIST PATIENTS

RESEARCH PLAN

1 YEAR PLAN

  1. Generate data at each transplant center specifying:

    1. When a patient was the final refusal factor for a transplant.

    2. What state that patient was in (MELD score, demographics, psychographics, notable psychological states)

  2. Conduct interviews with surgeons and patient coordinators at each transplant center to derive:

    1. Typical interaction models

    2. Center-based patient coordination models

  3. Work with UNOS to understand if the volume of patient refusals recorded reflect an impactful volume on utilization.

    1. Develop a database that includes geographic (by center), demographic and psychographic data on patients who refuse a liver while on the waiting list.  

    2. Use this data to create psychographic profiles to bucket ‘refusers’ into categories that we can then study further.

10 YEAR PLAN

*Assuming the 1-year grant plan results in data that is useful & indicates an intervention is needed.*

 

  1. Continue to generate data at remaining transplant centers.

    1. Identify centers & regions have the most cases of patient level refusals

    2. Complete full-audit of national centers

  2. Organize the procured data 

    1. Identify geographic differences in organ refusal and acceptance.

    2. Generate a temporal view of patient level & f

  3. Gain approval for new patient-care methodologies to be tested

    1. Choose beachhead centers to focus on and deploy patient care. 

    2. Develop structured test to determine effectiveness of methodology 

    3. Iterate on methodology & plan a national roll-out for best performing methodology

  4. Partner with UNOS & SRTR 

    1. Understand the quantitative effect of patient refusals on liver utilization rates

    2. Implement new methodologies at ceUtilize patient profile mapping & visualization 

    3. Develop testable methodologies to employ at a center-level within the surgeon-patient level interaction.

      1. Conversational training & guidance for surgeons

      2. Emotional support and preparation for patients

REFERENCES

 Orman, Eric S et al. “Declining liver utilization for transplantation in the United States and the impact of donation after cardiac death” 

 Davis, A. E., Mehrotra, S., Kilambi, V., Kang, J., McElroy, L., Lapin, B., Holl, J., Abecassis, M., Friedewald, J. J., … Ladner, D. P. (2014). The effect of the Statewide Sharing variance on geographic disparity in kidney transplantation in the United States. Clinical journal of the American Society of Nephrology : CJASN, 9(8), 1449-60.

 Gerber, D. A., Arrington, C. J., Taranto, S. E., Baker, T. and Sung, R. S. (2010), DonorNet and the Potential Effects on Organ Utilization. American Journal of Transplantation, 10: 1081-1089. doi:10.1111/j.1600-6143.2010.03036.x

Geneugelijk K, Spierings E. Matching donor and recipient based on predicted indirectly recognizable human leucocyte antigen epitopes. Int J Immunogenet. 2018;45:41–53. https://doi.org/10.1111/iji.12359

 Luscalov S., Loga L., Luscalov D., Lăcătuș A., Dragomir G., Dican L. (2017) Algorithm with Heuristics for Kidney Allocation in Transplant Information System. In: Vlad S., Roman N. (eds) International Conference on Advancements of Medicine and Health Care through Technology; 12th - 15th October 2016, Cluj-Napoca, Romania. IFMBE Proceedings, vol 59. Springer, Cham

https://unos.org/data/

https://liverfoundation.org/, https://transplants.org/, https://www.nlfindia.com/

https://unos.org/transplantation/matching-organs/ 

https://optn.transplant.hrsa.gov/data/view-data-reports/national-data/

UNOS.org waitlist by organ type, 2.12.19

https://optn.transplant.hrsa.gov/

https://optn.transplant.hrsa.gov/media/2339/opo_boardreport_organplacement_201712.pdf

https://optn.transplant.hrsa.gov/media/2339/opo_boardreport_organplacement_201712.pdf

https://www.ncbi.nlm.nih.gov/pubmed/27906775

https://onlinelibrary-wiley-com.ezp-prod1.hul.harvard.edu/doi/full/10.1111/ajt.13971 

and other primary research through interviews